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食品研究与开发:2024,45(4):158-163
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鸡蛋中氟虫腈及其代谢物残留分析方法
(1.中国热带农业科学院农产品加工研究所,农业农村部农产品加工质量安全风险评估实验室(湛江),广东湛江 524001;2.云南农业大学热带作物学院,云南普洱 665000)
Analytical Method of Fipronil and Its Metabolites Residue in Eggs
(1.Agricultural Products Processing Research Institute,Chinese Academy of Tropical Agricultural Sciences,Laboratory of Quality and Safety Risk Assessment on Agro-products Processing(Zhanjiang),Ministry of Agriculture and Rural Affairs,Zhanjiang 524001,Guangdong,China;2.College of Tropical Crops,Yunnan Agricultural University,Puer 665000,Yunnan,China)
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投稿时间:2023-09-22    
中文摘要: 采用超高效液相色谱-串联质谱技术,以鸡蛋为研究对象,建立一种高效测定鸡蛋中氟虫腈及其代谢物残留测定方法。样品用乙腈提取,并通过固相萃取技术对样品进行净化处理,供超高效液相色谱-串联质谱仪进行检测。实验结果表明,该方法能够有效地分离和测定氟虫腈及其代谢物残留。在3 个不同的添加水平(0.005、0.010、0.020 mg/kg)下,4 种农药的回收率范围为81.3%~92.5%,相对标准偏差均低于5.5%。;在0.001~0.050 μg/mL 浓度范围内,线性关系良好,相关系数均大于0.990 4;氟虫腈、氟甲腈、氟虫腈砜与氟虫腈亚砜的检出限分别为0.001、0.002、0.002、0.002 mg/kg。该方法准确、灵敏、快速,可满足对鸡蛋中氟虫腈、氟甲腈、氟虫腈砜与氟虫腈亚砜4 种药物残留的检测需要。
Abstract:An efficient and precise method utilizing ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was developed to quantify fipronil and its metabolites in eggs. The extraction of samples was performed using acetonitrile,followed by solid phase extraction column cleanup to prepare them for UPLC-MS/MS analysis.The results showed good separation,and that the average recovery of fipronil and its metabolites at 0.005,0.010 mg/kg and 0.020 mg/kg was between 81.3% and 92.5%,and the relative standard deviations were below 5.5%. Significant linear correlations were found between the peak area and the concentration of fipronil and its metabolites within the range of 0.001-0.050 μg/mL,with correlation coefficients surpassing 0.990.4. The limit of detection of fipronil,fipronil-desulfinyl,fipronil-sulfide,and fipronilsulfone was 0.001,0.002,0.002 mg/kg and 0.002 mg/kg,respectively.The UPLC-MS/MS method was accurate,sensitive and rapid and can detect fipronil and its metabolites in eggs.
文章编号:202404021     中图分类号:    文献标志码:
基金项目:农业农村部财政专项农产品质量安全监管风险评估项目(GJFP2019012、GJFP2019013、GJFP2019019);2023年海南省自然科学基金项目(323MS092)
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